Hang Sheng
Papers
1
Total Citations
16
H-Index
1
About
Hang Sheng is a researcher whose work lies at the intersection of computer vision, robotics, and intelligent systems. His key research areas include camera calibration for mobile robots and neural network optimization within intelligent spaces. Sheng’s major contribution is the development of a novel camera calibration method that leverages a neural network with a tunable activation function (TAF), addressing a critical challenge in enabling mobile robots to perceive and navigate their environments accurately. This approach, detailed in his most-cited paper from 2013, replaces traditional calibration techniques with a more flexible, learning-based model that adapts to complex spatial conditions. By adopting an inner product mode in the synapse model’s output signal calculation, his work enhances the precision and robustness of visual sensing in intelligent spaces. With 16 citations, this foundational paper has informed subsequent research in adaptive robotic perception. Sheng’s achievements demonstrate a practical fusion of neural network theory and real-world robotics applications, offering a scalable solution for autonomous systems that require reliable spatial awareness.
Research Focus
Key Achievements
Top Papers
- 1